Interrupted Time Series Design: A Useful Approach for Studying Interventions Targeting Participation
Bibliographic record
Abstract
Comparative effectiveness research (CER) strives for evidence to inform clinical decisions for specific clients in typical health care settings. While the randomized controlled trial (RCT) is well-aligned with the objectives of CER, this design may not be feasible or applicable to all research questions, particularly those pertaining to clinical decision making and individually based change. It is important, therefore, to consider alternative approaches, especially when studying complex and subjective outcomes such as children's participation. We propose the use of interrupted time series (ITS) quasi-experimental design for its potential application in determining the effectiveness of participation-focused interventions. In this perspective, ITS design is described, ascertaining its advantages and limitations, and suggestions are provided to overcome challenges to implementation. Results of a case study using the ITS approach to evaluate changes in participation of an adolescent with physical disabilities are described. Finally, strategies to implement this design in practice are suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".